Prodigy-ML-04

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Crafted With Love by Sam Naveenkumar .V

Overview

Welcome to Prodigy-ML-04, a comprehensive hand gesture recognition model designed to accurately identify and classify different hand gestures from image or video data. This project aims to enable intuitive human-computer interaction and gesture-based control systems, making technology more accessible and interactive.

Features

• Accurate Gesture Recognition: Leverage a convolutional neural network (CNN) to classify hand gestures with high accuracy.
• Real-time Predictions: Integrate with webcam or video feed to provide real-time gesture recognition.
• Easy to Use: Simple and clean code structure for easy understanding and modification.
• Extensible: Easily extend the model to include more gestures or improve accuracy with additional data.

Dataset

This project uses the Leap Gesture Database from Kaggle, which can be found here.

Getting Started

Prerequisites

• A Google account to use Google Colab.
• Basic understanding of Python and machine learning concepts.

Using the Colab Notebook

1. Open the Colab Notebook:

2. Setup Kaggle API Key:

   To download the dataset, you'll need a Kaggle API key. Follow these steps:

   • Sign in to your Kaggle account.
   • Go to Account and select Create New API Token to download the kaggle.json file.
   • Upload the kaggle.json file in the Colab notebook.

3. Run the Notebook:

   Execute each cell in the notebook sequentially. The notebook includes:

   • Downloading the Dataset: Automatically download and unzip the dataset from Kaggle.
   • Training the Model: Train the hand gesture recognition model using the provided dataset.
   • Real-time Predictions: Use the webcam to make real-time gesture predictions or upload an image/video for prediction.

4. Saving and Loading the Model:

   The trained model will be saved to your Google Drive. You can load the saved model for future predictions without retraining.

Model Architecture

The model uses a convolutional neural network (CNN) with the following architecture:

• Conv2D: 32 filters, 3x3 kernel, ReLU activation
• MaxPooling2D: 2x2 pool size
• Conv2D: 64 filters, 3x3 kernel, ReLU activation
• MaxPooling2D: 2x2 pool size
• Conv2D: 128 filters, 3x3 kernel, ReLU activation
• MaxPooling2D: 2x2 pool size
• Flatten
• Dense: 128 units, ReLU activation
• Dropout: 50%
• Dense: 8 units, Softmax activation (for 8 gesture classes)

Contributing

Contributions are welcome! Please feel free to submit a Pull Request or open an Issue to improve this project.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgements

• The dataset used in this project is provided by Leap Gesture Database on Kaggle.
• Special thanks to the contributors and the open-source community for their valuable resources and support.

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